A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder

Abstract Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoen...

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Autores principales:	Zixiang Luo, Chenyu Xu, Zhen Zhang, Wenfei Jin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/30dec251c0cd4590a65a1c8076f91bcf
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id	oai:doaj.org-article:30dec251c0cd4590a65a1c8076f91bcf
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spelling	oai:doaj.org-article:30dec251c0cd4590a65a1c8076f91bcf2021-12-02T18:01:40ZA topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder10.1038/s41598-021-99003-72045-2322https://doaj.org/article/30dec251c0cd4590a65a1c8076f91bcf2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99003-7https://doaj.org/toc/2045-2322Abstract Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (scGAE), a dimensionality reduction method that preserves topological structure in scRNA-seq data. scGAE builds a cell graph and uses a multitask-oriented graph autoencoder to preserve topological structure information and feature information in scRNA-seq data simultaneously. We further extended scGAE for scRNA-seq data visualization, clustering, and trajectory inference. Analyses of simulated data showed that scGAE accurately reconstructs developmental trajectory and separates discrete cell clusters under different scenarios, outperforming recently developed deep learning methods. Furthermore, implementation of scGAE on empirical data showed scGAE provided novel insights into cell developmental lineages and preserved inter-cluster distances.Zixiang LuoChenyu XuZhen ZhangWenfei JinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zixiang Luo Chenyu Xu Zhen Zhang Wenfei Jin A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
description	Abstract Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (scGAE), a dimensionality reduction method that preserves topological structure in scRNA-seq data. scGAE builds a cell graph and uses a multitask-oriented graph autoencoder to preserve topological structure information and feature information in scRNA-seq data simultaneously. We further extended scGAE for scRNA-seq data visualization, clustering, and trajectory inference. Analyses of simulated data showed that scGAE accurately reconstructs developmental trajectory and separates discrete cell clusters under different scenarios, outperforming recently developed deep learning methods. Furthermore, implementation of scGAE on empirical data showed scGAE provided novel insights into cell developmental lineages and preserved inter-cluster distances.
format	article
author	Zixiang Luo Chenyu Xu Zhen Zhang Wenfei Jin
author_facet	Zixiang Luo Chenyu Xu Zhen Zhang Wenfei Jin
author_sort	Zixiang Luo
title	A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
title_short	A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
title_full	A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
title_fullStr	A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
title_full_unstemmed	A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
title_sort	topology-preserving dimensionality reduction method for single-cell rna-seq data using graph autoencoder
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/30dec251c0cd4590a65a1c8076f91bcf
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A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder

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